code: def register_series(
      series,
      ref,
      pipeline=None,
      series_affine=None,
      ref_affine=None,
      static_mask=None,
  ):
      """Register a series to a reference image.
  
      Parameters
      ----------
      series : 4D array or nib.Nifti1Image class instance or str
          The data is 4D with the last dimension separating different 3D volumes
  
      ref : int or 3D array or nib.Nifti1Image class instance or str
          If this is an int, this is the index of the reference image within the
          series. Otherwise it is an array of data to register with (associated
          with a `ref_affine` required) or a nifti img or full path to a file
          containing one.
  
      pipeline : sequence, optional
          Sequence of transforms to do for each volume in the series.
          Default: (executed from left to right):
          `[center_of_mass, translation, rigid, affine]`
  
      series_affine, ref_affine : 4x4 arrays, optional.
          The affine. If provided, this input will over-ride the affine provided
          together with the nifti img or file.
  
      static_mask : array, shape (S, R, C) or (R, C), optional
          static image mask that defines which pixels in the static image
          are used to calculate the mutual information.
  
      Returns
      -------
      xformed, affines : 4D array with transformed data and a (4,4,n) array
      with 4x4 matrices associated with each of the volumes of the input moving
      data that was used to transform it into register with the static data.
  
      """
      pipeline = pipeline or [
          "center_of_mass",
          "translation",
          "rigid",
          "affine",
      ]
  
      series, series_affine = read_img_arr_or_path(
          series, affine=series_affine
      )
      if isinstance(ref, numbers.Number):
          ref_as_idx = ref
          idxer = np.zeros(series.shape[-1]).astype(bool)
          idxer[ref] = True
          ref = series[..., idxer].squeeze()
          ref_affine = series_affine
      else:
          ref_as_idx = False
          ref, ref_affine = read_img_arr_or_path(ref, affine=ref_affine)
          if len(ref.shape) != 3:
              raise ValueError(
                  "The reference image should be a single volume",
                  " or the index of one or more volumes",
              )
  
      xformed = np.zeros(series.shape)
      affines = np.zeros((4, 4, series.shape[-1]))
      for ii in range(series.shape[-1]):
          this_moving = series[..., ii]
          if (
              isinstance(ref_as_idx, numbers.Number)
              and ii == ref_as_idx
          ):
              # This is the reference! No need to move and the xform is I(4):
              xformed[..., ii] = this_moving
              affines[..., ii] = np.eye(4)
          else:
              transformed, reg_affine = affine_registration(
                  this_moving,
                  ref,
                  moving_affine=series_affine,
                  static_affine=ref_affine,
                  pipeline=pipeline,
                  static_mask=static_mask,
              )
              xformed[..., ii] = transformed
              affines[..., ii] = reg_affine
  
      return xformed, affines
  
imports: from warnings import warn
  import re
  import collections.abc
  from functools import partial
  import numbers
  import numpy
  import nibabel
  from dipy.align.metrics import CCMetric, EMMetric, SSDMetric
  from dipy.align.imwarp import SymmetricDiffeomorphicRegistration, DiffeomorphicMap
  from dipy.align.imaffine import transform_centers_of_mass, AffineMap, MutualInformationMetric, AffineRegistration
  from dipy.align.transforms import TranslationTransform3D, RigidTransform3D, RigidScalingTransform3D, RigidIsoScalingTransform3D, AffineTransform3D
  import dipy.core.gradients
  import dipy.data
  from dipy.align.streamlinear import StreamlineLinearRegistration
  from dipy.tracking.streamline import set_number_of_points
  from dipy.tracking.utils import transform_tracking_output
  from dipy.io.streamline import load_trk
  from dipy.io.utils import read_img_arr_or_path
  from dipy.io.image import load_nifti, save_nifti
label_desc: Use image node_id 8 for calling a global function with example usage: register_series(series, ref, pipeline, series_affine, ref_affine, static_mask) and returns: xformed,  affines